SoroushVahidi/scidocs huggingface dataset:
A 330nA energy-harvesting charger with battery management for solar and thermoelectric energy harvesting
(2012)

Harvesting energy from ambient energy sources such as solar and thermal gradients is one solution to address the dramatic increase in energy consumption of personal electronics. In this paper, an ultra low quiescent current charger and battery management IC that can efficiently extract energy from solar panels and thermoelectric generators to charge batteries and super capacitors is presented. While previous works on energy harvesting [1-5] report efficient DC-DC converters to extract the energy from the harvester, not all of them provide complete battery management functionality for various chemistries. Also, maximum power point tracking (MPPT) which is critical in energy harvesting applications is not provided in some of the published work. In this paper, a charger and battery management IC with 330nA quiescent current is presented. The IC can cold start from 330mV and 5μW of input power. The charger achieves efficiency greater than 80% at single cell solar voltages of 0.5V. A low quiescent current battery management architecture involving sampled circuits, sub regulated rails and clock gating is demonstrated.
On the Design and Scalability of Distributed Shared-Data Databases
(2015)

Database scale-out is commonly implemented by partitioning data across several database instances. This approach, however, has several restrictions. In particular, partitioned databases are inflexible in large-scale deployments and assume a partition-friendly workload in order to scale. In this paper, we analyze an alternative architecture design for distributed relational databases that overcomes the limitations of partitioned databases. The architecture is based on two fundamental principles: We decouple query processing and transaction management from data storage, and we share data across query processing nodes. The combination of these design choices provides scalability, elasticity, and operational flexibility without making any assumptions on the workload. As a drawback, sharing data among multiple database nodes causes synchronization overhead. To address this limitation, we introduce techniques for scalable transaction processing in shared-data environments. Specifically, we describe mechanisms for efficient data access, concurrency control, and data buffering. In combination with new hardware trends, the techniques enable performance characteristics that top state-of-the-art partitioned databases.
High-gain 60-GHz patch antenna array using GaN MMIC technology
(2015)

In this paper, a high-gain 2×2 antenna array at 60 GHz using GaN MMIC process technology is presented. For gain enhancement, a modified patch antenna element with two parasitic strips on each non-radiation edge and a parasitic patch on one of the radiation edges is proposed. This antenna element achieves a gain of 3.42 dBi at 60 GHz, about 2 dBi higher than a conventional patch antenna element using the same process technology. The 2×2 antenna array consisting of four modified patch antenna element exhibits a 10-dB impedance bandwidth of 10.1% and a peak gain of 5.7 dBi at 60 GHz.
Increasing psychological well-being and resilience by psychotherapeutic methods.
(2009)

A specific psychotherapeutic strategy for increasing psychological well-being and resilience, well-being therapy, has been developed and validated in a number of randomized controlled trials. The findings indicate that flourishing and resilience can be promoted by specific interventions leading to a positive evaluation of one's self, a sense of continued growth and development, the belief that life is purposeful and meaningful, the possession of quality relations with others, the capacity to manage effectively one's life, and a sense of self-determination. A decreased vulnerability to depression and anxiety has been demonstrated after well-being therapy in high-risk populations. There are important implications for the state/trait dichotomy in psychological well-being and for the concept of recovery in mood and anxiety disorders.
MIMO for ATSC 3.0
(2016)

This paper provides an overview of the optional multiple-input multiple-output (MIMO) antenna scheme adopted in ATSC 3.0 to improve robustness or increase capacity via additional spatial diversity and multiplexing by sending two data streams in a single radio frequency channel. Although it is not directly specified, it is expected in practice to use cross-polarized 2×2 MIMO (i.e., horizontal and vertical polarization) to retain multiplexing capabilities in line-of-sight conditions. MIMO allows overcoming the channel capacity limit of single antenna wireless communications in a given channel bandwidth without any increase in the total transmission power. But in the U.S. MIMO can actually provide a larger comparative gain because it would be allowed to increase the total transmit power, by transmitting the nominal transmit power in each polarization. Hence, in addition to the MIMO gains (array, diversity, and spatial multiplexing), MIMO could exploit an additional 3 dB power gain. The MIMO scheme adopted in ATSC 3.0 re-uses the single-input single-output antenna baseline constellations, and hence it introduces the use of MIMO with non-uniform constellations.
Adversarial support vector machine learning
(2012)

Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper. We consider two attack models: a free-range attack model that permits arbitrary data corruption and a restrained attack model that anticipates more realistic attacks that a reasonable adversary would devise under penalties. We then develop optimal SVM learning strategies against the two attack models. The learning algorithms minimize the hinge loss while assuming the adversary is modifying data to maximize the loss. Experiments are performed on both artificial and real data sets. We demonstrate that optimal solutions may be overly pessimistic when the actual attacks are much weaker than expected. More important, we demonstrate that it is possible to develop a much more resilient SVM learning model while making loose assumptions on the data corruption models. When derived under the restrained attack model, our optimal SVM learning strategy provides more robust overall performance under a wide range of attack parameters.
New Algorithms for SIMD Alignment
(2007)

Optimizing programs for modern multiprocessor or vector platforms is a major important challenge for compilers today. In this work, we focus on one challenging aspect: the SIMD ALIGNMENT problem. Previously, only heuristics were used to solve this problem, without guarantees on the number of shifts in the obtained solution. We study two interesting and realistic special cases of the SIMD ALIGNMENT problem and present two novel and efficient algorithms that provide optimal solutions for these two cases. The new algorithms employ dynamic programming and a MIN-CUT/MAX-FLOW algorithm as subroutines. We also discuss the relation between the SIMD ALIGNMENT problem and the MULTIWAY CUT and NODE MULTIWAY CUT problems; and we show how to derive an approximated solution to the SIMD ALIGNMENT problem based on approximation algorithms to these two known problems.
Toward normative expert systems: Part I. The Pathfinder project.
(1992)

Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.
Field and Torque Calculation and Transient Analysis in Variable Reluctance Machines
(2017)

The variable reluctance machine (VRM), also known as the switched reluctance machine, is a class of electrical machines that exhibits many interesting characteristics. However, the satisfactory analytical formulation of its working equations (torque and voltage equations) in a saturated regime is not yet completely available for covering some of its operational aspects, such as the analysis of transient state. This paper provides such equations for the general case in which a VRM operates with a pronounced level of magnetic saturation. These equations together with help of lookup tables allow online access of the instant torque on the shaft as well as the flux linkage of a phase, since the computation times are reduced to a few microseconds, whereas in a numerical approach such as the finite-element method, would take a few minutes of CPU time, which is totally inappropriate for such investigations.
Interactions between auditory and dorsal premotor cortex during synchronization to musical rhythms
(2006)

When listening to music, we often spontaneously synchronize our body movements to a rhythm's beat (e.g. tapping our feet). The goals of this study were to determine how features of a rhythm such as metric structure, can facilitate motor responses, and to elucidate the neural correlates of these auditory-motor interactions using fMRI. Five variants of an isochronous rhythm were created by increasing the contrast in sound amplitude between accented and unaccented tones, progressively highlighting the rhythm's metric structure. Subjects tapped in synchrony to these rhythms, and as metric saliency increased across the five levels, louder tones evoked longer tap durations with concomitant increases in the BOLD response at auditory and dorsal premotor cortices. The functional connectivity between these regions was also modulated by the stimulus manipulation. These results show that metric organization, as manipulated via intensity accentuation, modulates motor behavior and neural responses in auditory and dorsal premotor cortex. Auditory-motor interactions may take place at these regions with the dorsal premotor cortex interfacing sensory cues with temporally organized movement.
Vision Assisted SCARA Manipulator Design and Control Using Arduino and LabVIEW
(2017)

Using vision systems with SCARA robotic manipulators is beneficial in many industrial applications specially in automated object picking and placing tasks. In this paper, the equations of forward and inverse kinematics of a SCARA manipulator are derived using D-H parameters convention. The mechanical structure, as well as the electrical, electronic and pneumatic components of the manipulator were designed and developed. The manipulator's control software, animation and simulation were developed using LabVIEW and Arduino. In addition to that, a vision system was developed and integrated with the manipulator system where a mobile phone camera was used to acquire images, then transmit them wirelessly to LabVIEW's Vision Assistant codes for processing and calibration. The developed vision system allowed detection and calculation of object positions within the manipulator's workspace. Finally, the manipulator's positioning errors were determined and discussed based on a number of repeatability experiments.
Students' Misconceptions and Other Difficulties in Introductory Programming: A Literature Review
(2017)

Efforts to improve computer science education are underway, and teachers of computer science are challenged in introductory programming courses to help learners develop their understanding of programming and computer science. Identifying and addressing students’ misconceptions is a key part of a computer science teacher's competence. However, relevant research on this topic is not as fully developed in the computer science education field as it is in mathematics and science education. In this article, we first review relevant literature on general definitions of misconceptions and studies about students’ misconceptions and other difficulties in introductory programming. Next, we investigate the factors that contribute to the difficulties. Finally, strategies and tools to address difficulties including misconceptions are discussed.
 Based on the review of literature, we found that students exhibit various misconceptions and other difficulties in syntactic knowledge, conceptual knowledge, and strategic knowledge. These difficulties experienced by students are related to many factors including unfamiliarity of syntax, natural language, math knowledge, inaccurate mental models, lack of strategies, programming environments, and teachers’ knowledge and instruction. However, many sources of students’ difficulties have connections with students’ prior knowledge. To better understand and address students’ misconceptions and other difficulties, various instructional approaches and tools have been developed. Nevertheless, the dissemination of these approaches and tools has been limited. Thus, first, we suggest enhancing the dissemination of existing tools and approaches and investigating their long-term effects. Second, we recommend that computing education research move beyond documenting misconceptions to address the development of students’ (mis)conceptions by integrating conceptual change theories. Third, we believe that developing and enhancing instructors’ pedagogical content knowledge (PCK), including their knowledge of students’ misconceptions and ability to apply effective instructional approaches and tools to address students’ difficulties, is vital to the success of teaching introductory programming.
A 0.2–3.6-GHz 10-dBm B1dB 29-dBm IIP3 Tunable Filter for Transmit Leakage Suppression in SAW-Less 3G/4G FDD Receivers
(2015)

A tunable N-path filter with more than one decade tuning range is demonstrated to address transmit (TX) leakage in a surface-acoustic-wave-less diversity path receiver for frequency-division duplexing cellular systems. The proposed filter simultaneously creates a reject band and passband anywhere from 0.2 to 3.6 GHz to suppress TX leakage while minimizing insertion loss of the receive signal and is implemented in 45-nm CMOS silicon-on-insulator. Measurements show the 3-dB bandwidth of the passband is greater than 80 MHz with an independently tunable reject band, providing the ultimate rejection is from 33 to 41 dB while the passband insertion loss is between 2.6 dB and 4.3 dB over the tuning range. The proposed filter offers 29-dBm out-of-band (OOB) third-order input-intercept power (IIP3) and 22-dBm in-band (IB) IIP3 with a 10-dBm blocker 1-dB compression point (B1dB). To the authors' knowledge, this is the highest B1dB, IB IIP3, and OOB IIP3 for a CMOS tunable filter.
A Li-Ion Battery Charger With Smooth Control Circuit and Built-In Resistance Compensator for Achieving Stable and Fast Charging
(2010)

A built-in resistance compensator (BRC) technique is presented to speed up the charging time of a lithium-ion battery. A smooth control circuit (SCC) is proposed to ensure the stable transition from the constant-current (CC) to the constant-voltage (CV) stage. Due to the external parasitic resistance of the Li-ion battery-pack system, the charger circuit switches from the CC to the CV stage without fully charging the cell. The BRC technique dynamically estimates the external resistance to extend the CC stage. The experimental results show that the period of the CC stage can be extended to 40% of that of the original design. The charging time is effectively reduced.
Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller
(2001)

A fuzzy-controlled active state-of-charge controller (FC-ASCC) for improving the charging behavior of a lithium–ion (Li–ion) battery is proposed. The proposed FC-ASCC is designed to replace the general constant-voltage charging mode by two kinds of modes: sense and charge. A fuzzy-controlled algorithm is built with the predicted charger performance to program the charging trajectory faster and to remain the charge operation in a proposed safe-charge area (SCA). A modeling work is conducted for analyzing and describing the Li–ion battery in charging process. A three-dimensional Y-mesh diagram for describing the charging trajectories of the proposed FC charger is simulated. A prototype of a Li–ion battery charger with FC-ASCC is simulated and realized to assess the predicted charging performance. Experiment shows that the charging speed of the proposed FC charger compared with the general one increases about 23% and the charger can safely work in the SCA.
Accurate, Compact, and Power-Efficient Li-Ion Battery Charger Circuit
(2006)

A novel, accurate, compact, and power-efficient lithium-ion (Li-Ion) battery charger designed to yield maximum capacity, cycle life, and therefore runtime is presented and experimentally verified. The proposed charger uses a diode to smoothly (i.e., continuously) transition between two high-gain linear feedback loops and control a single power MOS device, automatically charging the battery with constant current and then constant voltage. An adaptive power-efficient charging scheme in the form of a cascaded switching regulator supply ensures the voltage across the charging power-intensive pMOS remains low, thereby reducing its power losses and yielding up to 27% better overall power efficiency. An 83% power-efficient printed circuit board prototype was built and used to charge several Li-Ion batteries to within plusmn0.43% of their optimum full-charge voltage and therefore within a negligibly small fraction of their full capacity
Secure data transfer in IoT environment: Adopting both cryptography and steganography techniques
(2016)

Owing to the unprecedented growth in computing power, electronics miniaturization and mobile and wireless network interconnections the internet has metamorphosed into Internet of Things (IoT) which refers to next stage of the information revolution whose context involves billions of individuals and devices interconnected to facilitate exchange of huge volume of data and information from diverse locations, demanding the consequent necessity for smart data aggregation followed by an increased obligation to index, hoard and process data with higher efficiency and effectiveness. But along with its myriad offered benefits and applications, emerges a novel complexity aspect in terms of many inherent hassles primarily security concerns during data transfer phases in IoT covering mostly data confidentiality and integrity features. Thus to enhance safe data transfer in smart IoT environment, a security scheme is proposed in this paper which addresses both the aforesaid issues, employing an integrated approach of lightweight cryptography and steganography (Simple LSB Substitution) technique during data transfer between IoT device and Home Server and adoption of combined approach of cryptography and steganography (Proposed MSB-LSB Substitution) technique during data transfer phases between Home Server and Cloud Servers.
Seizure classification in EEG signals utilizing Hilbert-Huang transform
(2011)

BACKGROUND
Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.


METHOD
Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.


RESULTS
The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.


CONCLUSION
An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.
Transfer Learning in Multi-Agent Reinforcement Learning Domains
(2011)

Transfer learning refers to the process of reusing knowledge from past tasks in order to speed up the learning procedure in new tasks. In reinforcement learning, where agents often require a considerable amount of training, transfer learning comprises a suitable solution for speeding up learning. Transfer learning methods have primarily been applied in single-agent reinforcement learning algorithms, while no prior work has addressed this issue in the case of multi-agent learning. This work proposes a novel method for transfer learning in multi-agent reinforcement learning domains. We test the proposed approach in a multiagent domain under various setups. The results demonstrate that the method helps to reduce the learning time and increase the asymptotic performance.
Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons
(2018)

This paper introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon, and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, MPQA Subjectivity Lexicon, General Inquirer, NRC Word-Sentiment Association Lexicon and SO-CAL lexicon. The effectiveness of the sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset and a news headlines dataset. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorization, obtaining accuracy rates of 75% to 77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words, for both document-level and sentencelevel sentiment categorization. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorization task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts, and the WKWSCI lexicon for non-review texts.